Kinematic Control

Kinematic control focuses on planning and executing the desired movement of robotic systems and vehicles without explicitly considering forces or masses. Current research emphasizes developing robust and computationally efficient control algorithms, often employing model-based approaches like Lyapunov-based controllers, differential kinematics, and model predictive control (MPC) with various architectures including bio-inspired neural networks and fuzzy logic systems to address challenges such as actuator saturation, constraint satisfaction (e.g., Remote Center of Motion in surgery), and handling of uncertainties in dynamic environments. These advancements are crucial for improving the performance, safety, and adaptability of robots in diverse applications, from minimally invasive surgery and autonomous driving to collaborative manipulation and multi-robot coordination.